7 Safer Defaults for Code Review Automation (No AI) [2026]

Originally published at kunalganglani.com — read it there for inline code, hero image, and live links.

Code review automation safer defaults are the boring, deterministic rules that move “mechanical” feedback (formatting, lint, tests, ownership routing) out of human brains and into your toolchain. They matter right now because review load keeps creeping up, and the industry’s reflex is to paper over it with AI reviewers instead of fixing the workflow gaps that create noisy PRs in the first place.

Most teams don’t need an LLM to tell them there’s trailing whitespace. They need a merge gate that makes trailing whitespace impossible to ship.

What is code review automation (and why I’m avoiding AI here)

Code review automation is anything in your workflow that turns repeatable review comments into deterministic checks. Formatting, linting, unit tests, secret scanning, ownership routing, merge rules. Stuff that can be evaluated the same way every time.

I’m not anti-AI. I run this site on a 7-agent publishing pipeline with a deterministic quality gate. The lesson that keeps slapping me in the face is one the hype merchants hate: deterministic gates catch more than doubling the review model’s size. If the rule is crisp, you want a crisp machine enforcing it. It’s predictable, inspectable, and easy to rip out when it causes problems.

AI review tools fail in a couple ways that actually matter on real teams:

1. They create false confidence. A bot comment looks authoritative, even when it’s guessing.
2. They increase review surface area. More comments, more threads, more time. Still no guarantee the important stuff got eyes.

If you’re in a regulated environment, there’s a third failure mode. You now get to explain to auditors why a probabilistic black box is part of your change-control process. That’s not an engineering decision anymore. That’s a paperwork lifestyle.

The non-AI path is less glamorous. It’s also the one you can standardize across repos, enforce with branch protections, and debug when something goes sideways.

Automate anything that’s objective. Reserve humans for judgment.

Safer defaults for code review automation (no AI)

Here’s the checklist I reach for when a team asks for “code review automation safer defaults” and what they actually mean is “we’re drowning in PR churn.”

1. Auto-format on save and in CI so whitespace never hits the diff.
2. Lint for correctness, not taste (ban risky patterns, don’t litigate style).
3. Run fast checks at commit-time (pre-commit, lint-staged, etc.).
4. Require unit tests to pass on every PR into your default branch.
5. Add a basic security baseline (at minimum: secret scanning; often: dependency scanning).
6. Route reviews with CODEOWNERS so the right people see the right changes.
7. Enforce it with branch protection / rulesets: required reviews + required status checks.

That’s the core. Everything else is tuning.

Here’s the heuristic I care about more than any specific tool choice. Keep the “default PR gate” fast enough that developers don’t start negotiating with it. If your required checks regularly take more than a few minutes, you’ll get bypass pressure. Not because your team is lazy. Because you built a bottleneck and then acted surprised when people tried to walk around it.

If you want the workflow version of “reduce blast radius,” read my write-up on stacked PRs. Smaller diffs make every automation choice safer.

Formatting first: the lowest-risk way to reduce review noise

Formatting is the easiest win in this whole space. It’s high leverage and almost entirely non-controversial. Which is why it’s hilarious how many teams still treat it like an optional preference.

As the Prettier maintainers say, the point is to end style debates and free reviewers to focus on substance. In practice, it also kills a quieter tax. Attention is finite. Every nit about indentation steals attention from the one subtle bug hiding in the diff.

Two defaults that keep formatting from backfiring:

• Format-only commits. If you’re introducing Prettier/gofmt/black into a legacy codebase, do it in a dedicated PR and merge it fast. Don’t mix reformatting with functional changes. You’re not proving a point. You’re making review harder.
• Use a blame-ignore file for mass reformatting. Git has blame.ignoreRevsFile. It’s one of those tiny quality-of-life moves that stops “who wrote this?” archaeology from becoming useless after a big reformat.

A rule of thumb in legacy repos. If enabling formatting makes normal PR diffs feel “twice as big,” you introduced it wrong. Don’t argue with your team’s perception. Fix the rollout.

This is also why I’m strict about deterministic output in my own automation. I had a slug rewrite incident in my publishing pipeline that burned 907K impressions of link equity in one shot. The fix wasn’t “smarter AI.” It was treating identity as a one-way door and gating it before publish.

If you’re dealing with a messy repo, I’d rather see formatting enforced only on touched files than a repo-wide big bang. You can always tighten later.

Linting and static analysis: chase signal, not taste

Linting is where teams accidentally build a bureaucracy and call it “quality.”

The right mental model is: a linter is a policy engine for things that are objectively wrong or objectively risky. Not a tool for re-litigating how curly braces should look.

Use linters to enforce:

• Actual bugs: unused variables, shadowed names, unreachable code.
• Unsafe patterns: eval, SQL string concat, dangerous deserialization.
• Consistency that affects correctness: exhaustive switch handling, nullability checks.

Be careful with:

• Complexity rules (cyclomatic complexity thresholds, max parameters). These can be useful, but they’re loud in legacy code.
• “Prefer X” style rules that don’t change behavior. These create resentment because they feel like aesthetic policing.

A concrete default for small teams. Pick one linter per language and keep the rule set small. For JavaScript/TypeScript, that might be ESLint plus TypeScript’s compiler options. For Go, go vet and staticcheck. For Python, ruff plus mypy if you’re actually using types.

Then do the part everyone tries to skip. Decide what warns versus what fails the build.

My heuristic:

• Fail the PR on rules that catch bugs or security issues.
• Warn only on “cleanup” rules until you’ve paid down enough debt.

If you’re enforcing lint on a repo with years of baggage, stage it:

1. Turn it on in CI in “report-only” mode.
2. Fix the worst 20% of violations that cause 80% of the noise.
3. Flip to “fail on changed lines/files.”
4. Only then consider repo-wide fail.

This isn’t about being nice. It’s about preventing your linter from becoming a merge hostage situation.

Pre-commit hooks: shift left without making dev machines miserable

Pre-commit hooks work for a simple reason. They move the cheapest feedback earlier.

A reviewer is expensive. CI is slower. A local check at commit-time is basically free.

The pre-commit framework is explicit about the goal. Catch simple issues before code review so reviewers can focus on architecture instead of trivia. It’s also practical in polyglot repos because it can install and run hooks even if the developer doesn’t have that language runtime installed locally.

If you want pre-commit to be a “safer default,” you need to respect a few constraints:

• Keep hooks fast. My rule of thumb is sub-10 seconds added to a normal commit on the common path. Longer than that and people will bypass.
• Auto-fix locally, not in CI. Let formatters fix files on the developer’s machine. CI should enforce, not surprise-edit.
• Make it reproducible. Pin tool versions in the hook config so “it passed on my machine” doesn’t become a weekly ritual.

This ties directly into security too. I wrote a step-by-step setup for gitleaks + pre-commit + CI because secrets are the one class of mistake where you want the earliest possible tripwire.

CODEOWNERS safer defaults (without creating bottlenecks)

A CODEOWNERS file is a routing table for review. GitHub can use it to automatically request reviews from the owners of specific paths. Pair it with branch protection and you can require approval from code owners before merge. That’s straight from the GitHub docs on CODEOWNERS.

Used well, CODEOWNERS cuts two kinds of waste:

• Random reviewer selection (“who’s around?”) that produces shallow review.
• Missed expertise where a risky change ships because nobody who understands that area saw it.

Used poorly, it creates a single point of failure. Congrats. You built a queue.

Safer defaults I like:

• Prefer teams over individuals. Put @org/payments-team not @alice.
• Cap critical paths to 2–5 owners. One owner is brittle. Ten owners is a committee.
• Always have an escalation path. Define what happens if owners don’t respond in, say, 4 business hours. Rotation, backups, or a “reviewer of the day” model.
• Avoid “everything is owned by the platform team.” That’s not ownership. That’s a holding pen.
• Document exceptions. Emergency hotfixes happen. Decide up front what “break glass” looks like, and log it.

One subtle trick. Use CODEOWNERS to route reviews, but don’t require code owner approval for everything by default. Require it where blast radius is real: auth, billing, infra, data migrations.

This connects nicely with my post on AI coding team workflow policy. Even without AI, the failure mode is the same. Too many changes, too little attention.

Branch protection and rulesets: turn checks into a real merge gate

Automation that isn’t enforced is a suggestion.

GitHub’s protected branches can require PR reviews and require status checks to pass before merging. That’s the backbone of a safe merge gate. The GitHub docs on protected branches lay out the menu.

The more important question is: what’s the default configuration that doesn’t wreck velocity?

My opinionated defaults (small team vs regulated team)

Below is the table I wish more docs had. It’s not “best practice.” It’s defaults that behave well under stress.

Two details matter more than the exact checklist:

• Fail-closed vs fail-open. If a required check is down, do merges block? In regulated environments, you usually want fail-closed with a documented emergency procedure. In early-stage teams, fail-open can be acceptable for some checks, but only if you log it.
• Rulesets to reduce drift. GitHub’s newer “rulesets” let you standardize policy across branches and repos. The value isn’t another checkbox. It’s eliminating the situation where Repo A requires tests and Repo B quietly doesn’t.

Which status checks should be required before merging a PR?

The safest baseline I’ve found is:

• format check
• lint / static analysis
• unit tests
• secret scanning

Then you add based on risk:

• integration tests (often slow, but high signal)
• dependency scanning / license scanning
• SAST rules that you’ve tuned to your codebase

The meta-rule: don’t make a check required until it’s stable. If a check is flaky, you’re training the team to ignore red lights.

For engineering leadership folks, this is the same reliability mindset as any other production gate. I treat CI like a production system because it controls whether code ships.

Danger zones: where “automation” quietly lowers your bar

Most workflow write-ups stop at “add checks.” The real failure modes show up a month later.

Flaky CI that blocks merges

If a required check fails for reasons unrelated to the PR, your gate becomes random. Random gates get bypassed.

My operational heuristic is blunt. If a required check blocks merges for more than one workday in a week, you have a reliability incident. Treat it like one.

Mitigations that actually work:

• Quarantine the flaky suite (make it non-required) until it’s fixed.
• Add retries only with instrumentation. Blind retries just hide failures.
• Separate “required fast lane” checks from “slow confidence” checks.

Auto-fix bots and drive-by PRs

Auto-fix PRs (formatting, lint fixes, dependency bumps) can be great. They also create review fatigue. The danger is merging them because they look “safe,” then discovering they touched the wrong thing.

My default: auto-fix is allowed, auto-merge is not, unless the change is narrowly scoped and has strong tests.

If you want to use CI to apply fixes, be careful with tools that open PRs automatically. If you can’t explain the change in one sentence, it’s not a bot PR. It’s a normal PR.

Rubber-stamping approvals

When approvals are required, teams sometimes optimize for throughput by approving without reading. That’s worse than having no rule at all, because it gives leadership a dashboard that says “reviewed.”

Two countermeasures:

• Make PRs small. Stacked PRs help. So does feature flagging.
• Put real friction only on high-risk paths. Requiring 2 approvals on every typo trains rubber-stamping.

Silent bypass paths

Every gate has a bypass. The question is whether it’s visible.

Safer defaults:

• Only a small set of admins can bypass branch protection.
• Bypasses require a reason.
• You review bypass logs weekly, the same way you’d review incident tickets.

This is where automation becomes governance. If you can’t measure bypass frequency, you don’t have a policy. You have vibes.

Rolling this out in a legacy repo (without giant diffs)

Legacy repos are where good intentions go to die. The trick is incremental enforcement.

Here’s a rollout path I’ve used repeatedly because it avoids diff explosions and “stop the world” refactors:

1. Formatting first, isolated. One PR. Merge it. Add a blame-ignore revision. Make formatter required.
2. Add lint in report-only mode. Collect violations for a week.
3. Flip lint to “changed files only.” Fail PRs only when new violations are introduced.
4. Introduce pre-commit hooks for the fast checks (format, basic lint, secret scan). Keep them optional for a sprint, then enforce.
5. Add CODEOWNERS routing and start with “request review,” not “require approval,” except on truly sensitive paths.
6. Turn on branch protection / rulesets with required status checks. Add required approvals last.

Rollback guidance matters. My rule: any new required check must be removable in under 10 minutes. If your “oops” path requires editing 12 repos, you built something fragile.

A good forcing function is to treat this like any other production change. Roll out to one repo, measure impact, then standardize.

If you want the automation mindset applied to AI systems, the same “gates first” philosophy shows up in my posts on production AI and AI in production. The mechanics differ, but the sociology is identical.

What humans should still review (because automation can’t)

Google’s code review guidance is refreshingly direct about what reviewers should look for: design, functionality, complexity, tests, naming, comments, style, documentation. The key takeaway is that humans should focus on correctness and maintainability, not mechanical fixes.

Here’s my more opinionated split.

Automate:

• formatting
• basic lint and static analysis
• unit tests + smoke tests
• secret scanning and dependency policy baselines
• ownership routing and merge requirements

Keep humans on:

• architecture and long-term maintainability
• security threat modeling for new surfaces
• data migrations and backward compatibility
• product behavior and UX edge cases
• operational risk (rollbacks, feature flags, observability)

A concrete place this shows up in my work. When I built the SOC 2 scaffolding CLI at Rise People, the big win was baking compliance into scaffolding instead of trying to “review” compliance into existence at PR time. Gates beat heroics.

My prediction: the next year of “AI code review” will feel like email filters

The next 12 months are going to be full of AI review bots that feel like spam filters. Some will catch real issues. Many will generate plausible noise. Teams will add them, then quietly turn them off.

The winners won’t be the teams with the cleverest AI. They’ll be the teams with the most boring, reliable merge gates and the discipline to keep humans doing the parts only humans can do.

If you’re leading an engineering org, here’s the challenge. Pick one repo this week and implement the 7 safer defaults above. Then track two numbers for 30 days: median time-to-merge and bypass count. If both go down, you don’t need AI review hype. You need to roll the pattern out everywhere.

Originally published on kunalganglani.com